Natural human-computer interaction for virtual personal assistant systems

ABSTRACT

Technologies for natural language interactions with virtual personal assistant systems include a computing device configured to capture audio input, distort the audio input to produce a number of distorted audio variations, and perform speech recognition on the audio input and the distorted audio variants. The computing device selects a result from a large number of potential speech recognition results based on contextual information. The computing device may measure a user&#39;s engagement level by using an eye tracking sensor to determine whether the user is visually focused on an avatar rendered by the virtual personal assistant. The avatar may be rendered in a disengaged state, a ready state, or an engaged state based on the user engagement level. The avatar may be rendered as semitransparent in the disengaged state, and the transparency may be reduced in the ready state or the engaged state. Other embodiments are described and claimed.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of U.S. application Ser.No. 14/129,435, entitled “NATURAL HUMAN-COMPUTER INTERACTION FOR VIRTUALPERSONAL ASSISTANT SYSTEMS,” which was filed on Dec. 26, 2013 and whichis a national stage entry under 35 USC §371(b) of InternationalApplication No. PCT/US2013/041866, which was filed May 20, 2013.

BACKGROUND

Virtual personal assistants are artificial intelligence systems thatperform tasks on a computing device in response to natural-languagerequests from a user. For example, a virtual personal assistant mayhandle calendaring, reminders, and messaging tasks for the user. Tointeract with the virtual personal assistant, the user typically entersa pre-defined input sequence on the computing device, for examplepressing a dedicated hardware button or speaking a predefined code word.The user may enter natural-language requests through conventional textinput or through speech recognition.

To further facilitate natural interaction, many virtual personalassistants display a humanlike character, also known as an avatar, toserve as a main point of interaction with the user. The avatar mayoccupy or obscure a significant portion of the display of the computingdevice. Further, the avatar may interfere with use of other applicationson the computing device, particularly when the user did not intend toactivate the avatar. Even when displaying a humanlike avatar, typicalsystems may not fully model natural human interaction, and instead mayrequire conventional human-computer interactions such as button presses,mouse clicks, or the like.

Speech recognition systems convert spoken utterances of the user intocomputer-readable representations of text. Typical speech recognitionsystems attempt to determine a single most-likely speech recognitionresult for a given audio input. Such systems may filter out noise orotherwise attempt to enhance the audio input signal in order to improvespeech recognition results. Some systems may provide a small number ofalternative results; however, these results are typically only slightvariations on each other. Typical speech recognition engines may beimplemented as components of a local computing device, or as servicesprovided by a server computing device.

BRIEF DESCRIPTION OF THE DRAWINGS

The concepts described herein are illustrated by way of example and notby way of limitation in the accompanying figures. For simplicity andclarity of illustration, elements illustrated in the figures are notnecessarily drawn to scale. Where considered appropriate, referencelabels have been repeated among the figures to indicate corresponding oranalogous elements.

FIG. 1 is a simplified block diagram of at least one embodiment of acomputing device for natural interaction with a virtual personalassistant;

FIG. 2 is a simplified block diagram of at least one embodiment of anenvironment of the computing device of FIG. 1;

FIG. 3 is a simplified block diagram of at least one embodiment ofsystem for natural interaction with a virtual personal assistant;

FIG. 4 is a simplified flow diagram of at least one embodiment of amethod for introducing audio distortion to improve speech recognitionthat may be executed by the computing device of FIGS. 1 and 2;

FIG. 5 is a simplified flow diagram of at least one embodiment of amethod for introducing audio distortion to improve speech recognitionthat may be executed by the speech recognition server of FIG. 4; and

FIGS. 6A and 6B are a simplified flow diagram of at least one embodimentof a method for natural interaction with a virtual personal assistantthat may be executed by the computing device of FIGS. 1 and 2.

DETAILED DESCRIPTION OF THE DRAWINGS

While the concepts of the present disclosure are susceptible to variousmodifications and alternative forms, specific embodiments thereof havebeen shown by way of example in the drawings and will be describedherein in detail. It should be understood, however, that there is nointent to limit the concepts of the present disclosure to the particularforms disclosed, but on the contrary, the intention is to cover allmodifications, equivalents, and alternatives consistent with the presentdisclosure and the appended claims.

References in the specification to “one embodiment,” “an embodiment,”“an illustrative embodiment,” etc., indicate that the embodimentdescribed may include a particular feature, structure, orcharacteristic, but every embodiment may or may not necessarily includethat particular feature, structure, or characteristic. Moreover, suchphrases are not necessarily referring to the same embodiment. Further,when a particular feature, structure, or characteristic is described inconnection with an embodiment, it is submitted that it is within theknowledge of one skilled in the art to effect such feature, structure,or characteristic in connection with other embodiments whether or notexplicitly described.

The disclosed embodiments may be implemented, in some cases, inhardware, firmware, software, or any combination thereof. The disclosedembodiments may also be implemented as instructions carried by or storedon a transitory or non-transitory machine-readable (e.g.,computer-readable) storage medium, which may be read and executed by oneor more processors. A machine-readable storage medium may be embodied asany storage device, mechanism, or other physical structure for storingor transmitting information in a form readable by a machine (e.g., avolatile or non-volatile memory, a media disc, or other media device).

In the drawings, some structural or method features may be shown inspecific arrangements and/or orderings. However, it should beappreciated that such specific arrangements and/or orderings may not berequired. Rather, in some embodiments, such features may be arranged ina different manner and/or order than shown in the illustrative figures.Additionally, the inclusion of a structural or method feature in aparticular figure is not meant to imply that such feature is required inall embodiments and, in some embodiments, may not be included or may becombined with other features.

Referring now to FIG. 1, an illustrative computing device 100 fornatural interaction with a virtual personal assistant includes aprocessor 120, an I/O subsystem 122, and memory 124. The computingdevice 100 captures audio input and obtains speech recognition resultsfrom a speech recognition engine that are based on distortions appliedto the audio input. The applied distortions may result in multiple,semantically distinct variations of the audio input. Supplying multiplevariations may allow for the speech recognition engine to produce manymore potential speech recognition results, which in turn may increasespeech recognition accuracy. The speech recognition engine may operateon the computing device 100, or in some embodiments, on a remote speechrecognition server as discussed in more detail below. The speechrecognition results may be used to control a virtual personal assistant.The virtual personal assistant models the engagement level of the userby tracking the user's visual focus and/or by interpreting the user'sspeech. Modeling user engagement may allow the virtual personalassistant to engage in more-natural human interactions; for example, thevirtual personal assistant may better determine when it is beingaddressed by the user or when the user has moved on to some other task.The virtual personal assistant may represent the engagement level of theuser by adjusting the size, position, and/or transparency of an avataron a display screen. Rendering the engagement level in this manner mayalso facilitate natural interaction by allowing the user to betterunderstand the state of the virtual personal assistant withoutunnecessarily interrupting the user's other work.

The computing device 100 may be embodied as any type of device capableof performing the functions described herein. For example, the computingdevice 100 may be embodied as, without limitation, a smartphone, acellular phone, a tablet computer, a notebook computer, a laptopcomputer, a desktop computer, a distributed computing system, amultiprocessor system, a consumer electronic device, a smart appliance,and/or any other computing device capable of recognizing spoken usercommands. As shown in FIG. 1, the illustrative computing device 100includes the processor 120, the I/O subsystem 122, the memory 124, and adata storage device 126. Of course, the computing device 100 may includeother or additional components, such as those commonly found in aportable computer (e.g., various input/output devices), in otherembodiments. Additionally, in some embodiments, one or more of theillustrative components may be incorporated in, or otherwise from aportion of, another component. For example, the memory 124, or portionsthereof, may be incorporated in the processor 120 in some embodiments.

The processor 120 may be embodied as any type of processor currentlyknown or developed in the future and capable of performing the functionsdescribed herein. For example, the processor may be embodied as a singleor multi-core processor(s), digital signal processor, microcontroller,or other processor or processing/controlling circuit. Similarly, thememory 124 may be embodied as any type of volatile or non-volatilememory or data storage currently known or developed in the future andcapable of performing the functions described herein. In operation, thememory 124 may store various data and software used during operation ofthe computing device 100 such as operating systems, applications,programs, libraries, and drivers. The memory 124 is communicativelycoupled to the processor 120 via the I/O subsystem 122, which may beembodied as circuitry and/or components to facilitate input/outputoperations with the processor 120, the memory 124, and other componentsof the computing device 100. For example, the I/O subsystem 122 may beembodied as, or otherwise include, memory controller hubs, input/outputcontrol hubs, firmware devices, communication links (i.e.,point-to-point links, bus links, wires, cables, light guides, printedcircuit board traces, etc.) and/or other components and subsystems tofacilitate the input/output operations. In some embodiments, the I/Osubsystem 122 may form a portion of a system-on-a-chip (SoC) and beincorporated, along with the processor 120, the memory 124, and othercomponents of the computing device 100, on a single integrated circuitchip.

The data storage 126 may be embodied as any type of device or devicesconfigured for short-term or long-term storage of data such as, forexample, memory devices and circuits, memory cards, hard disk drives,solid-state drives, or other data storage devices. The data storage 126may store program and data files relating to the virtual personalassistant, and may serve as temporary or permanent storage for audiodata captured by the computing device 100.

The computing device 100 further includes a display 128, an audio sensor130, and an eye tracking sensor 132. The display 128 of the computingdevice 100 may be embodied as any type of display capable of displayingdigital information such as a liquid crystal display (LCD), a lightemitting diode (LED), a plasma display, a cathode ray tube (CRT), orother type of display device. In some embodiments, the display 128 maybe coupled to a touch screen to receive user input.

The audio sensor 130 may be embodied as any sensor capable of capturingaudio signals such as a microphone, a line input jack, ananalog-to-digital converter (ADC), or other type of audio sensor. Theaudio sensor 130 may be used by the computing device 100 to detectspeech commands uttered by the user, as described below.

The eye tracking sensor 132 may be embodied as any one or more sensorscapable of determining an area on the display 128 of the computingdevice 100 on which the user's eyes are focused. For example, the eyetracking sensor 132 may be embodied as a digital camera or a digitaldepth camera capable of tracking the focus of the user's gaze. In otherembodiments, the eye tracking sensor 132 may be embodied as activeinfrared emitters and infrared detectors capable of tracking the user'seye movements over time. In those embodiments, the eye tracking sensor132 may capture the infrared light reflected off of various internal andexternal features of the user's eye and thereby calculate the directionof the user's gaze. The eye tracking sensor 132 may also be capable ofdetermining the position of the user's head in three-dimensional space.In some embodiments, an eye tracking sensor 132 such as a depth cameramay be capable of determining head position data directly. In otherembodiments, the eye tracking sensor 132 may be used with another sensorsuch as a video camera to calculate the position of the user's head.

In some embodiments, the computing device 100 may also include one ormore peripheral devices 134. The peripheral devices 134 may include anynumber of additional sensors, input/output devices, interface devices,and/or other peripheral devices. For example, in some embodiments, theperipheral devices 134 may include a touch screen, graphics circuitry,keyboard, mouse, speaker system, interface devices, and/or otherinput/output devices. In some embodiments, the peripheral devices 134may be used along with the eye tracking sensor 132 and/or the audiosensor 130 to determine the user's engagement level. As another example,in some embodiments, the peripheral devices 134 may include acommunication circuit, device, or collection thereof capable of enablingcommunications between the computing device 100 and other remote serversand/or devices.

Referring now to FIG. 2, in one embodiment, the computing device 100establishes an environment 200 during operation. The illustrativeembodiment 200 includes a speech recognition module 202, an audio inputmodule 204, a virtual personal assistant 208, and an engagement module214. The various modules of the environment 200 may be embodied ashardware, firmware, software, or a combination thereof.

The speech recognition module 202 is configured to perform speechrecognition on audio input data received from the audio input module204. The speech recognition module 202 ranks and filters speechrecognition results to produce a single result or a ranked list oflikely results. The speech recognition module 202 may use a speechrecognition grammar provided by an application such as the virtualpersonal assistant 208 to rank and filter speech recognition results. Insome embodiments, the speech recognition module 202 may recognize speechin a dictation or free speech mode. The dictation or free speech modemay use a full natural language vocabulary and grammar to recognizeresults, and thus may produce additional likely speech recognitionresults.

The audio input module 204 captures audio input data from the audiosensor 130 and applies audio distortions to the audio input data toproduce multiple variations of the audio input. The audio distortionsmay modify amplitude, frequency, duration, and/or other characteristicsof the audio input to produce semantic variation among the distortedaudio variations. The audio input module 204 provides the distortedaudio variations to the speech recognition module 202. In someembodiments, those functions may be performed by sub-modules, forexample, by a distortion module 206. Additionally, in some embodiments,the functionality of the speech recognition module 202 and/or thedistortion module 206 may be performed by a remote server, for exampleby a cloud service, as described below in connection with FIG. 3.

The virtual personal assistant 208 responds to spoken user commands anddisplays an avatar on the display 128 to provide information on thestatus of the virtual personal assistant 208. The virtual personalassistant 208 may maintain a speech recognition grammar defining spokencommands that may be accepted from the user, including commandvocabulary and syntax. The avatar is a character or other visualrepresentation of the virtual personal assistant 208. The avatar mayinclude human-like characteristics such as facial features or a humanform. Those human-like features may facilitate natural interaction withthe user. In some embodiments, those functions may be performed bysub-modules, for example a command module 210 or an avatar module 212.In some embodiments, the virtual personal assistant 208 may be usedwithout speech recognition; that is, the virtual personal assistant 208may respond to non-speech input such as typed input or input gestures.

The engagement module 214 determines the user's level of engagement withthe virtual personal assistant 208 based on sensor data received fromthe eye tracking sensor 132 and/or the audio sensor 130. For example,the engagement module 214 may determine the level of engagement based onhow long or how often the user's eyes focus on the avatar. In someembodiments, the engagement module 214 may also analyze speechrecognition results from the speech recognition module 202 to determinethe user's level of engagement. The engagement module 214 provides theengagement level to the virtual personal assistant 208, allowing thevirtual personal assistant 208 to modify the avatar accordingly.

Referring now to FIG. 3, in some embodiments, the speech recognitionmodule 202 and/or the distortion module 206 may be embodied in a remotespeech recognition server 300. The speech recognition server 300 isconfigured to provide services including performing speech recognitionanalysis on audio input transmitted from the computing device 100 over anetwork 302. The speech recognition server 300 may be embodied as anytype of server computing device, or collection of devices, capable ofperforming the functions described herein. As such, the speechrecognition server 300 may include components and features similar tothe computing device 100 such as a processor, I/O subsystem, memory,data storage, communication circuitry, and various peripheral devices,which are not illustrated in FIG. 3 for clarity of the presentdescription. Further, the speech recognition server 300 may be embodiedas a single server computing device or a collection of servers andassociated devices. For example, in some embodiments, the speechrecognition server 300 may be embodied as a “virtual server” formed frommultiple computing devices distributed across a network and operating ina public or private cloud. Accordingly, although the speech recognitionserver 300 is illustrated in FIG. 3 and described below as embodied as asingle server computing device, it should be appreciated that the speechrecognition server 300 may be embodied as multiple devices cooperatingtogether to facilitate the functionality described below.

As discussed above, the speech recognition module 202 and, in someembodiments, the audio distortion module 206 are established on thespeech recognition server 300 (i.e., rather than, or in addition to, thecomputing device 100). The computing device 100 includes the audio inputmodule 204, the virtual personal assistant 208, the engagement module214, and in some embodiments, the distortion module 206. The variousmodules of the speech recognition server 300 and the computing device100 perform the same functions as the modules described above inconnection with FIG. 2, and may be embodied as hardware, firmware,software, or a combination thereof.

The illustrative computing device 100 of FIG. 3 further includes aspeech analysis module 304. The speech analysis module 304 is configuredto transmit the audio input, and in some embodiments, the distortedaudio variations to the speech recognition server 300. The speechanalysis module 304 is also configured to receive speech recognitionresults from the speech recognition server 300 and may select a resultfrom the speech recognition results based on contextual information.

In embodiments in which the speech recognition module 202 and thedistortion module 206 are located on the speech recognition server 300,the audio input module 204 of the computing device 100 captures audioinput from the audio sensor 130, and the speech analysis module 304sends the audio input to the speech recognition server 300. In suchembodiments, the distortion module 206 of the speech recognition server300 receives the audio input and applies audio distortions to the audioinput data to produce multiple variations of the audio input, asdescribed above with regard to the computing device 100. The audio inputand the distorted audio variations are provided to the speechrecognition module 202 of the speech recognition server 300, whichperforms speech recognition as described above. The speech recognitionmodule 202 subsequently transmits the speech recognition results back tothe computing device 100. The speech recognition module 202 may transmita list of speech recognition results, or may transmit a single result.

Alternatively, in embodiments in which only the speech recognitionmodule 202 is located on the speech recognition server 300, the audioinput module 204 of the computing device 100 captures audio input fromthe audio sensor 130 and the distortion module 206 of the computingdevice 100 applies audio distortions to the audio input data to producemultiple variations of the audio input, as described above. In suchembodiments, the computing device 100 transmits the audio input and thedistorted audio variations to the speech recognition server 300. Theaudio input and the distorted audio variations are received by thespeech recognition module 202 of the speech recognition server 300,which performs speech recognition as described above. The speechrecognition module 202 transmits the speech recognition results back tothe computing device 100. The speech recognition module 202 may transmita list of speech recognition results, or may transmit a single result.

Referring now to FIG. 4, in use, the computing device 100 may execute amethod 400 for introducing audio distortion to improve speechrecognition. The method 400 begins with block 402, in which thecomputing device 100 captures audio input using the audio sensor 130.The audio input may be stored in any format useable for further analysisand manipulation, including compressed or uncompressed formats. Audiocapture may be initiated based on a request from the user or based on arequest from a component of the computing device 100. For example, audiocapture may be initiated when the user activates or engages the virtualpersonal assistant 208, as further described below.

In block 404, in some embodiments the computing device 100 maypre-process the captured audio input. As part of pre-processing, thecomputing device 100 may perform speech recognition on the capturedaudio input. The speech recognition results from pre-processing may beused to control distortion of the audio input, as described below.

In block 406, the computing device 100 distorts the audio input toproduce multiple distorted variations of the audio input. The audioinput is distorted in ways that may produce different and semanticallyvaried versions of the audio input. The distortions may modify, forexample, the amplitude, timing, pitch, or any other salientcharacteristic of the audio input. The computing device 100 may applythe distortions individually or in various combinations. Each variationmay be stored as separate audio data, or the computing device 100 mayapply distortions to the original audio input as needed.

For example, in some embodiments, the computing device 100 may removequiet audio segments from the audio input in block 408. To do so, thecomputing device 100 may identify internal segments of the audio input(that is, segments that are not at the beginning or the end of the audioinput) having an amplitude below a threshold amplitude and delete thoseinternal segments from the audio input. After removing a quiet segment,segments of the audio input that were formerly on either side of thequiet segment are appended together or otherwise run together.Therefore, this distortion may eliminate pauses between utterancesspoken by the user, which may combine the utterances into a single word.

In some embodiments, the computing device 100 may expand quiet audiosegments in the audio input in block 410. To do so, the computing device100 may identify segments of the audio input having an amplitude below athreshold amplitude and increase the duration of those segments. Afterexpanding the quiet segments, neighboring segments of the audio inputare spaced further apart. Therefore, this distortion may increase theduration of pauses between utterances spoken by the user, which maysplit the utterances into two distinct words.

In some embodiments, the computing device 100 may insert pauses at oneor more phonetic split points in the audio input identified inpre-processing in block 412. As with quiet audio segment expansion, thisdistortion may split utterances of the user into distinct words. Unlikequiet audio expansion, this distortion does not require an existingpause or quiet audio segment.

In some embodiments, in block 414, the computing device 100 may modifythe playback speed of the audio input; that is, the computing device 100may speed up or slow down the audio input. In some embodiments, in block416, the computing device 100 may apply other audio transformations tothe audio input. For example, the computing device 100 may alter thepitch of the audio input or mix the audio input with random noise. Suchdistortions may produce variation in speech recognition results. Ofcourse, the computing device 100 may apply additional or otherdistortions to the audio input.

In block 418, in embodiments in which the speech recognition module 202is located on the remote speech recognition server 300, the computingdevice 100 may transmit the audio input and the multiple distorted audiovariations to the speech recognition server 302. As described above, thespeech recognition server may provide speech recognition services.Offloading speech recognition tasks to the speech recognition server 302may improve efficiency for a computing device 100 having limitedcomputational power, for example, a smartphone.

After applying the various distortions to produce multiple variations ofthe audio input, the computing device 100 obtains speech recognitionresults based on the audio input and all of the multiple distorted audiovariations in block 420. For example, in embodiments in which the speechrecognition module 202 is located on the mobile computing device 100,the device 100 may perform speech recognition locally. In thoseembodiments, the computing device 100 may re-use or reference speechrecognition results created while pre-processing the audio input inblock 404 and perform speech recognition in block 420 on only thedistorted audio variations. In other embodiments in which the speechrecognition module 202 is located on the speech recognition server 300,the computing device 100 may receive speech recognition results from thespeech recognition server 300. However the speech recognition resultsare obtained, the speech recognition process produces multiple speechrecognition results; that is, multiple potential interpretations of thecaptured audio input. In some embodiments, the speech recognitionprocess may produce a large number of speech recognition results; forexample, hundreds or thousands of speech recognition results may beproduced. In some embodiments, the speech recognition results may begenerated using a conventional speech recognition engine supplied withthe multiple variations of the audio input repeatedly or in parallel.

In block 422, in some embodiments, the computing device 100 maydetermine semantically relevant speech recognition results from amongthe full speech recognition results produced in block 420. In otherwords, the computing device 100 may analyze the speech recognitionresults to find results that it “understands” and to reject results thatdo not “make sense.” For example, an application of the computing device100 such as the virtual personal assistant 208 may include a speechrecognition grammar. The speech recognition grammar may include a set ofactions, data objects, and other commands understood by the application.The computing device 100 may determine semantically relevant speechrecognition results by accepting only those results that satisfy thespeech recognition grammar.

In block 424, the computing device 100 selects a result from speechrecognition results based on contextual information. In someembodiments, the result may be selected from the smaller set ofsemantically relevant speech recognition results determined in block422. The selected result may be the speech recognition result mostrelevant to the current context of the user and/or the computing device100. The most relevant speech recognition result is most likely to bethe result intended by the user. The contextual information includes anyinformation that may reveal the intent of the user, and may include thestate of any applications currently executing on the computing device100, such as web browsers, productivity applications, or the virtualpersonal assistant 208. The contextual information may also includecontextual information associated with the user, such as a calendar,contact list, email account, or other personalized data. The contextualinformation may further include basic contextual information of thecomputing device 100 such as date, time, or location. Althoughillustrated as selecting a single result from the speech recognitionresults, in some embodiments the computing device 100 may produce a listof speech recognition results, which list may be sorted based on thecontextual information. After selecting a result, the method 400 iscompleted and control may be passed to a calling method that may operateon the speech recognition results. For example, as described below, thevirtual personal assistant 208 may act on the speech recognitionresults.

Referring now to FIG. 5, in embodiments in which the speech recognitionmodule 202 and/or the distortion module 206 are located on the speechrecognition server 300, the speech recognition server 300 may execute amethod 500 for introducing audio distortion to improve speechrecognition. The method 500 begins with block 502, in which the speechrecognition server 302 determines whether a request for speechrecognition has been received from a computing device 100. If not, themethod 500 loops back to block 502 to continue listening for speechrecognition requests. However, if a request has been received, themethod 500 advances to block 504 in which the speech recognition server300 receives audio input from computing device 100. The audio input waspreviously captured by the computing device 100 using the audio sensor130. The audio input may be stored in any format useable for furtheranalysis and manipulation, including compressed or uncompressed formats.

In block 506, in some embodiments the speech recognition server 300 maypre-process the received audio input. As part of pre-processing, thespeech recognition server 300 may perform speech recognition on thereceived audio input. The speech recognition results from pre-processingmay be used to control distortion of the audio input, as describedbelow.

In block 508, the speech recognition server 300 distorts the audio inputto produce multiple distorted variations of the audio input. The audioinput is distorted in ways that may produce different and semanticallyvaried versions of the audio input. The distortions may modify, forexample, the amplitude, timing, pitch, or any other salientcharacteristic of the audio input. The speech recognition server 300 mayapply the distortions individually or in various combinations. Eachvariation may be stored as separate audio data, or the speechrecognition server 300 may apply distortions to the original audio inputas needed.

For example, in some embodiments, the speech recognition server 300 mayremove quiet audio segments from the audio input in block 510, expandquiet audio segments in the audio input in block 512, insert pauses atone or more phonetic split points in the audio input identified inpre-processing in block 514, modify the playback speed of the audioinput in block 516, and/or apply other audio transforms to the audioinput in block 518 as described in detail above with regard to computingdevice 100 and block 406 of method 400. Of course, the speechrecognition server 300 may apply additional or other distortions to theaudio input. Regardless, after applying the various distortions toproduce multiple variations of the audio input, the speech recognitionserver 300 performs speech recognition on the audio input and all of themultiple distorted audio variations in block 520. In some embodiments,the speech recognition server 300 may re-use or reference speechrecognition results created while pre-processing the audio input inblock 506 and perform speech recognition in block 520 on only thedistorted audio variations. The speech recognition process producesmultiple speech recognition results; that is, multiple potentialinterpretations of the captured audio input. In some embodiments, thespeech recognition process may produce a large number of speechrecognition results; for example, hundreds or thousands of speechrecognition results may be produced. In some embodiments, the speechrecognition results may be generated using a conventional speechrecognition engine supplied with the multiple variations of the audioinput repeatedly or in parallel.

In block 522, in some embodiments, the speech recognition server 300 maydetermine semantically relevant speech recognition results from amongthe full speech recognition results produced in block 520. In otherwords, the speech recognition server 300 may analyze the speechrecognition results to find results that it “understands” and to rejectresults that do not “make sense.” For example, an application of thecomputing device 100 such as the virtual personal assistant 208 mayinclude a speech recognition grammar. The speech recognition grammar mayinclude a set of actions, data objects, and other commands understood bythe application. The speech recognition server 300 may determinesemantically relevant speech recognition results by accepting only thoseresults that satisfy the speech recognition grammar of the virtualpersonal assistant 208.

In block 524, in some embodiments, the speech recognition server 300 mayselect a result from speech recognition results based on contextualinformation. For example, in some embodiments, the result may beselected from the smaller set of semantically relevant speechrecognition results determined in block 522. The selected result may bethe speech recognition result most relevant to the current context ofthe user and/or the computing device 100. The most relevant speechrecognition result is most likely to be the result intended by the user.The contextual information may include any information that may revealthe intent of the user, and may include the state of any applicationscurrently executing on the computing device 100, such as web browsers,productivity applications, or the virtual personal assistant 208. Thecontextual information may also include contextual informationassociated with the user, such as a calendar, contact list, emailaccount, or other personalized data. The contextual information mayfurther include basic contextual information of the computing device 100such as date, time, or location. Although illustrated as selecting asingle result from the speech recognition results, in some embodimentsthe speech recognition server 302 may produce a list of speechrecognition results, which list may be sorted based on the contextualinformation.

In block 526, the speech recognition server 302 transmits the speechrecognition result or speech recognition results back to the computingdevice 100. The computing device 100 may then use the speech recognitionresult or speech recognition results to control an application such asthe virtual personal assistant 208. After transmitting, the method 500loops back to block 502 to listen for further speech recognitionrequests from the computing device 100.

Referring now to FIG. 6A, in use, the computing device 100 may execute amethod 600 for natural interaction with the virtual personal assistant208. The method 600 begins with block 602, in which an avatar of thevirtual personal assistant 208 is displayed in a disengaged state on thedisplay 128 of the computing device 100. As described above, the avataris a visual representation of the virtual personal assistant 208. Theavatar includes human-like or anthropomorphic features that mayfacilitate natural interaction with the user. For example, the avatarmay include an animated human or human-like face including an eye or apair of eyes. When in the disengaged state, the avatar is displayed in amanner that indicates that the virtual personal assistant 208 is idleand/or not actively listening to instructions. For example, the avatarmay be represented as sleeping or looking away from the user.

In some embodiments, in block 604, the avatar may be displayed in thedisengaged state as semitransparent, allowing background applications ofthe computing device 100 to shine through the avatar. Whensemitransparent, the avatar may remain visible to the user while stillallowing all of the display 128 to be useable by other applications andat least partially visible to the user. The semitransparent appearancemay be accomplished through alpha-blending the avatar and the otherapplications, compositing a scene, or by any other comparable techniquefor semitransparency. In some embodiments, in block 606, the avatar maybe displayed in the disengaged state in a relatively small size or at aposition away from the currently active application of the computingdevice 100. For example, the avatar may be rendered in a smaller size ina corner of the display 128, allowing the user to continue working inthe active application without distraction. In other embodiments, thecomputing device 100 may render the avatar in the disengaged state ascompletely invisible. In those embodiments, all of the display 128 maybe available for other applications; however, the user may not bepresented with any visual cues indicating the virtual personal assistant208 is available.

In block 608, the computing device 100 monitors for activation of thevirtual personal assistant 208 by the user. In some embodiments, inblock 610, the computing device 100 may receive eye tracking data fromthe eye tracking sensor 132. The computing device 100 interprets the eyetracking data to determine the position of the user's gaze on thedisplay 128. The user may indicate activation of the virtual personalassistant 208 by focusing on the position of the avatar currentlydisplayed in the disengaged state. The computing device 100 may filterthe eye tracking data to remove brief or spurious glances at the avatar.In some embodiments, in block 612, the computing device 100 may receiveaudio input from the audio sensor 130. The computing device 100interprets the audio input to determine whether the user has uttered acode word for activating the virtual personal assistant 208. Forexample, the code word may be embodied as the name of the virtualpersonal assistant 208, or a common word such as “assistant” or“computer.”

In block 614, the computing device 100 determines whether the user hasrequested activation of the virtual personal assistant 208. As describedabove, activation may be requested when the user's gaze has focused onthe avatar for a length of time longer than a certain threshold, or whenthe code word has been detected. If the user has not requestedactivation, the method 600 loops back to block 602, keeping the avatarin the disengaged state and continuing to monitor for activation. If theuser has requested activation, the method 600 advances to block 616.

In block 616, the computing device 100 displays the avatar in a readystate. The ready state indicates to the user that the virtual personalassistant 208 is available and ready to respond to user input. In block618, in some embodiments the computing device 100 may render the avataras making eye contact with the user. Eye contact is a powerful cue thatmay naturally convey to the user that the virtual personal assistant 208is ready for input. In some embodiments, the computing device 100 maysimply render the avatar as facing straight out of the display 128,because the user is typically positioned in front of the display 128. Insome embodiments, the computing device 100 may use eye tracking datareceived from the eye tracking sensor 132 to determine the location ofthe user's eyes and render the avatar as looking at the user's eyes. Thecomputing device 100 may render eye contact using anthropomorphic cuesto simulate natural interaction; for example, the computing device 100may render the avatar as periodically glancing away from the user, whichmay make the user less likely to interpret the avatar as staring at theuser. In some embodiments, in block 620 the avatar may be displayed inthe ready state by decreasing the transparency of the avatar; that is,by making the avatar appear more solid. While still allowing backgroundapplications to shine through, the increasingly solid appearance of theavatar may indicate that the virtual personal assistant 208 is ready toreceive commands In block 622, in some embodiments the computing device100 may display the avatar in the ready state by adjusting the position,size, and/or visibility of the avatar. For example, the avatar may bemoved toward the active application on the display 128, may be increasedin size, or may be made visible.

While the avatar is displayed in the ready state, in block 624 thecomputing device 100 determines the user's engagement level, that is,the level of interest the user is exhibiting in the avatar. Bydetermining the user's engagement level, the computing device 100 maydetermine in a natural manner whether or not the user intended toactivate the virtual personal assistant 208. In some embodiments, inblock 626 the computing device 100 may receive eye tracking data fromthe eye tracking sensor 132. The computing device 100 may analyze theeye tracking data to determine whether the user has visually focused onthe avatar. Visually focusing on the avatar may indicate a relativelyhigh engagement level, and focusing away from the avatar may indicaterelatively a low engagement level. The computing device 100 may requirethe user to visually focus on the avatar for a period of time greaterthan a threshold time, in order to filter out spurious glances.Similarly, the computing device 100 may ignore short glances away fromthe avatar to improve recognition performance, essentially applying alow-pass filter to the eye tracking data. In some embodiments, in block628 the computing device 100 may receive audio input from the audiosensor 130. The computing device 100 may analyze the audio input todetermine whether the user is addressing the virtual personal assistant208. In some embodiments, the computing device 100 may perform speechrecognition on the audio input.

In block 630, the computing device 100 determines whether the user isengaged with the avatar. The computing device 100 may determine whetheruser is engaged by comparing the user engagement level determined inblock 624 with a threshold engagement level. If not the user isdetermined not to be engaged, the method 600 loops back to block 602 torender the avatar in the disengaged state. To summarize thatinteraction, the avatar has unobtrusively indicated to the user that itis ready for interaction, determined that the user is not engaged withthe avatar, and then retreated away from the user's attention. Thus,that interaction may simulate a natural human interaction. Referringagain to block 630, if the user is engaged with the avatar, the method600 advances to block 632, illustrated in FIG. 6B.

Referring now to FIG. 6B, in block 632 the computing device 100 displaysthe avatar in an engaged state. The engaged state indicates to the userthat the virtual personal assistant 208 is actively interpretingcommands issued by the user. For example, in some embodiments, thecomputing device 100 may render the avatar as making eye contact withthe user in block 634. As described above in connection with block 618,the computing device 100 may receive eye tracking data from the eyetracking sensor 132 and render the avatar as looking toward the user'seyes. Additionally or alternatively, in some embodiments, the computingdevice 100 may decrease the transparency of the avatar in block 636. Forexample, the computing device 100 may render the avatar as fully opaquein some embodiments. Further, in some embodiments, the computing device100 may adjust the size and/or position of the avatar in block 638. Forexample, the avatar may be rendered close to or in front of thecurrently-active application on the display 128, or the avatar may beincreased in size. In some embodiments, although the avatar is displayedas opaque and in front of other applications on the display 128, theavatar does not intercept or otherwise interfere with user input,allowing the user to continue working with the computing device 100.

While displaying the avatar in the engaged state, execution of themethod 600 proceeds in parallel to blocks 640 and 644. In block 640, thecomputing device 100 performs speech recognition on audio input receivedfrom the audio sensor 130 while the avatar is in the engaged state. Insome embodiments, the computing device 100 may perform a more accurateor more computationally-intense speech recognition method while in theengaged state, because it is likely that the user is directly addressingthe virtual personal assistant 208. For example, the computing device100 may perform the speech recognition method of introducing distortiondescribed above in connection with FIG. 4, or any other speechrecognition technique.

In block 642, the computing device 100 determines whether or not acommand has been received that may be executed by the virtual personalassistant 208. The computing device 100 may apply the results of speechrecognition determined in block 640 to a speech recognition grammar ofthe virtual personal assistant 208 to determine whether a valid commandhas been issued. If no command has been received, the method 600 loopsback to block 640 to continue performing speech recognition. If acommand has been received, the method 600 advances to block 652 asdescribed below.

As described above, the method 600 executes block 644 in parallel withblock 640. In block 644, the computing device 100 monitors the userengagement level while in the engaged state. As described above inconnection with block 624, the computing device 100 determines the levelof interest the user is exhibiting in the avatar, which may allow formore natural interactions. In some embodiments, in block 646, thecomputing device 100 may receive eye tracking data from the eye trackingsensor 132. As described above with respect to block 626, the computingdevice 100 may determine the engagement level based on whether or notthe user's eyes are focused on the avatar. In some embodiments, in block648 the computing device 100 may receive audio input from the audiosensor 130. As described above in connection with block 628, thecomputing device 100 may analyze the audio input to determine whetherthe user is addressing the virtual personal assistant 208. In someembodiments, the computing device 100 may use speech recognition resultsfrom block 640 to determine whether the user is addressing the virtualpersonal assistant 208.

In block 650, the computing device 100 determines whether the user isengaged with the avatar. As described above in connection with block630, the computing device 100 may compare the user engagement leveldetermined in block 644 with a threshold engagement level. If the useris engaged with the avatar, the method 600 loops back to block 644 tocontinue monitoring the user engagement level. To summarize thatinteraction, if the user remains actively engaged with the avatar, forexample, by engaging in eye contact with the avatar, the avatar alsoremains in the engaged state. That interaction may simulate a naturalinteraction as when holding a conversation. If in block 650 thecomputing device 100 determines that the user is not engaged, the method600 loops back to block 602 of FIG. 6A to render the avatar in thedisengaged state. To summarize that interaction, if after some time ofengagement the user is no longer engaged, for example by turning back toother work, then the avatar also leaves, or begins to leave, the engagedstate. That interaction may simulate a natural interaction as when aperson completes a conversation.

When the method 600 advances from block 642 to block 652 or loops backfrom block 650 to block 602, the computing device 100 encounters a tasksynchronization boundary, illustrated in FIG. 6B by a thick line. Thetask synchronization boundary ensures that only one task of method 600remains active in the computing device 100. For example, when advancingfrom block 642 to block 652 based on a received speech command, thecomputing device 100 may terminate the task executing block 644, causingthe computing device 100 to stop monitoring the user engagement level.Similarly, when looping back from block 650 to block 602, the computingdevice 100 may terminate the task executing block 640, causing thecomputing device 100 to stop performing speech recognition. Further,although illustrated as being performed in parallel, in otherembodiments the tasks of performing speech recognition and monitoringthe user engagement level may be performed sequentially or in aninterleaved manner.

In block 652, the computing device 100 displays the avatar in a workingstate. The working state indicates to the user that the virtual personalassistant 208 is currently executing a task. In some embodiments, theworking state includes a representation of the task being performed, forexample, an application icon or a representation of the avatarperforming a task. In some embodiments, the avatar displayed in theworking state may be similar or identical to the avatar displayed in thedisengaged state; that is, the avatar displayed in the working state maybe unobtrusive and may not interfere with the user performing other workon the computing device 100. In some embodiments, in block 654 thecomputing device 100 may increase the transparency of the avatar on thedisplay 128. In some embodiments, in block 656, the computing device 100may adjust the size and/or position of the avatar. For example, thecomputing device 100 may decrease the size of the avatar or move theavatar way from a currently active application on the display 128.

In block 658, while the avatar is displayed in the working state thecomputing device 100 executes the command received from the user. Thecommand may be executed by the virtual personal assistant 208 or byvarious other applications of the computing device 100 controlled by thevirtual personal assistant 208. Further, in some embodiments, while theavatar is displayed in the working state, the computing device 100 maymonitor for activation of the assistant by the user, similar to asdescribed above in connection with FIG. 408. Monitoring for activationmay allow the user to interrupt the currently executing command orinitiate a new command.

After completion of the command, in block 660 the computing device 100determines whether to notify the user of completion. For example, ifexecution of the command produced displayable results or error messages,the computing device 100 may determine to notify the user. If thecomputing device 100 determines to notify the user, the method 600 loopsback to block 616 of FIG. 6A to display the avatar in the ready state.To summarize that interaction, the avatar works on a task unobtrusivelyin the background for some time and then notifies the user of completionin a natural manner. The user may then interact with the avatar asdescribed above to receive the notification. If in block 660 thecomputing device 100 determines not to notify the user, the method 600loops back to block 602 of FIG. 6A to display the avatar in thedisengaged state and await further activation. To summarize thatinteraction, the avatar works on a task unobtrusively in the backgroundfor some time and then returns to the disengaged state, indicating tothe user in a natural manner that the virtual personal assistant 208 isfree for further interactions.

In the preceding illustrative embodiment, the user engagement has beendescribed as a binary value—either engaged or not engaged. However, inother embodiments, the user engagement level may be measured on acontinuum. In those embodiments, the avatar may be displayed withproperties that reflect the value of the user engagement level. Forexample, the transparency of the avatar may be smoothly adjusted over arange of values to indicate the user engagement level. Similarly, thesize or position of the avatar may also be smoothly adjusted over arange of values to indicate the user engagement level.

EXAMPLES

Illustrative examples of the devices, systems, and methods disclosedherein are provided below. An embodiment of the devices, systems, andmethods may include any one or more, and any combination of, theexamples described below.

Example 1 includes a computing device for speech recognition, thecomputing device comprising an audio sensor; an audio input module tocapture audio input using the audio sensor; and distort the audio inputto produce a plurality of distorted audio variations; and a speechrecognition module to perform speech recognition on the audio input andeach of the distorted audio variations to produce a plurality of speechrecognition results; and select a result from the speech recognitionresults based on contextual information.

Example 2 includes the subject matter of Example 1, and wherein todistort the audio input comprises to remove an internal segment of theaudio input.

Example 3 includes the subject matter of any of Examples 1 and 2, andwherein the internal segment of the audio input comprises a segmenthaving an amplitude with a predefined relationship to an amplitudethreshold.

Example 4 includes the subject matter of any of Examples 1-3, andwherein to distort the audio input comprises to expand a length of asegment of the audio input having an amplitude with a predefinedrelationship to an amplitude threshold.

Example 5 includes the subject matter of any of Examples 1-4, whereinthe segment having an amplitude with the predefined relationship to theamplitude threshold comprises a segment having an amplitude below theamplitude threshold.

Example 6 includes the subject matter of any of Examples 1-5, andwherein to distort the audio input comprises to insert a pause at aphonetic split point of the audio input identified by performing speechrecognition on the audio input.

Example 7 includes the subject matter of any of Examples 1-6, andwherein to distort the audio input comprises at least one of to: (i)speed up the audio input, (ii) slow down the audio input, (iii) adjust apitch of the audio input, or (iv) introduce noise to the audio input.

Example 8 includes the subject matter of any of Examples 1-7, andwherein the plurality of speech recognition results comprises at leastone hundred speech recognition results.

Example 9 includes the subject matter of any of Examples 1-8, andfurther comprising one or more applications having a speech recognitiongrammar; wherein the speech recognition module is further to determinesemantically relevant results of the speech recognition results based onthe speech recognition grammar of the one or more applications; andwherein to select the result from the speech recognition resultscomprises to select a result from the semantically relevant results.

Example 10 includes the subject matter of any of Examples 1-9, andwherein the one or more applications comprise a virtual personalassistant.

Example 11 includes a computing device for speech recognition, thecomputing device comprising: an audio sensor; an audio input module to:capture audio input using the audio sensor; and distort the audio inputto produce a plurality of distorted audio variations; and a speechanalysis module to: transmit the audio input and the distorted audiovariations from the computing device to a speech recognition module;receive a plurality of speech recognition results from the speechrecognition module based on the audio input and the distorted audiovariations; and select a result from the speech recognition resultsbased on contextual information.

Example 12 includes the subject matter of Example 11, and wherein thespeech recognition module is located on a server remote from thecomputing device.

Example 13 includes the subject matter of any of Examples 11 and 12, andwherein to distort the audio input comprises to remove an internalsegment of the audio input.

Example 14 includes the subject matter of any of Examples 11-13, andwherein the internal segment of the audio input comprises a segmenthaving an amplitude with a predefined relationship to an amplitudethreshold.

Example 15 includes the subject matter of any of Examples 11-14, andwherein to distort the audio input comprises to expand a length of asegment of the audio input having an amplitude with a predefinedrelationship to an amplitude threshold.

Example 16 includes the subject matter of any of Examples 11-15, andwherein the segment having an amplitude with the predefined relationshipto the amplitude threshold comprises a segment having an amplitude belowthe amplitude threshold.

Example 17 includes the subject matter of any of Examples 11-16, andwherein to distort the audio input comprises to insert a pause at aphonetic split point of the audio input identified by performing speechrecognition on the audio input.

Example 18 includes the subject matter of any of Examples 11-17, andwherein to distort the audio input comprises at least one of to: (i)speed up the audio input, (ii) slow down the audio input, (iii) adjust apitch of the audio input, or (iv) introduce noise to the audio input.

Example 19 includes the subject matter of any of Examples 11-18, andwherein the plurality of speech recognition results comprises at leastone hundred speech recognition results.

Example 20 includes a speech recognition server for speech recognition,the speech recognition server comprising: a distortion module to:receive audio input captured by a computing device; and distort theaudio input to produce a plurality of distorted audio variations; and aspeech recognition module to: perform speech recognition on the audioinput and each of the distorted audio variations to produce a pluralityof speech recognition results; and transmit the plurality of speechrecognition results to the computing device.

Example 21 includes the subject matter of Example 20, and wherein todistort the audio input comprises to remove an internal segment of theaudio input.

Example 22 includes the subject matter of any of Examples 20 and 21, andwherein the internal segment of the audio input comprises a segmenthaving an amplitude with a predefined relationship to an amplitudethreshold.

Example 23 includes the subject matter of any of Examples 20-22, andwherein to distort the audio input comprises to expand a length of asegment of the audio input having an amplitude with a predefinedrelationship to an amplitude threshold.

Example 24 includes the subject matter of any of Examples 20-23, andwherein the segment having the amplitude with the predefinedrelationship to the amplitude threshold comprises a segment having anamplitude below the amplitude threshold.

Example 25 includes the subject matter of any of Examples 20-24, andwherein to distort the audio input comprises to insert a pause at aphonetic split point of the audio input identified by performing speechrecognition on the audio input.

Example 26 includes the subject matter of any of Examples 20-25, andwherein to distort the audio input comprises one of to: (i) speed up theaudio input, (ii) slow down the audio input, (iii) adjust a pitch of theaudio input, or (iv) introduce noise to the audio input.

Example 27 includes the subject matter of any of Examples 20-26, andwherein the plurality of speech recognition results comprises at leastone hundred speech recognition results.

Example 28 includes a computing device comprising: a display; a virtualpersonal assistant to display an avatar of the virtual personalassistant on the display in a disengaged state, a ready state, and anengaged state; and an engagement module to: determine whether a user ofthe computing device has requested activation of the virtual personalassistant while the avatar is displayed in the disengaged state; anddetermine an engagement level of the user while the avatar is displayedin the ready state; wherein the virtual personal assistant is to:display the avatar in the ready state in response to a determinationthat the user has requested activation of the virtual personalassistant; display the avatar in the engaged state in response to adetermination that the user has an engagement level greater than athreshold level; and display the avatar in the disengaged state inresponse to a determination that the user has an engagement level lessthan the threshold level.

Example 29 includes the subject matter of Example 28, and wherein theengagement module is further to monitor the engagement level of the userwhile the avatar is displayed in the engaged state.

Example 30 includes the subject matter of any of Examples 28-29, andfurther comprising an eye tracking sensor, wherein to determine whetherthe user has requested activation of the virtual personal assistantcomprises to: receive eye tracking data from the eye tracking sensor;and determine whether the user has focused on the avatar based on theeye tracking data.

Example 31 includes the subject matter of any of Examples 28-30, andfurther comprising an audio sensor, wherein to determine whether theuser has requested activation of the virtual personal assistantcomprises to: receive audio input from the audio sensor; and detect acode word uttered by the user based on the audio input.

Example 32 includes the subject matter of any of Examples 28-31, andfurther comprising an eye tracking sensor, wherein to determine theengagement level of the user comprises to: receive eye tracking datafrom the eye tracking sensor; determine, based on the eye tracking data,whether the user has visually focused on the avatar for a period of timegreater than a threshold time; determine that the user has an engagementlevel greater than the threshold level in response to a determinationthat the user has visually focused on the avatar for a period of timegreater than the threshold time; and determine that the user has anengagement level less than the threshold level in response to adetermination that the user has not visually focused on the avatar for aperiod of time greater than the threshold time.

Example 33 includes the subject matter of any of Examples 28-32, andwherein to determine whether the user has visually focused on the avatarfor a period of time greater than the threshold time comprises to ignoreglances away from the avatar for a second period of time less than asecond threshold time.

Example 34 includes the subject matter of any of Examples 28-33, andfurther comprising an audio sensor, wherein to determine the engagementlevel of the user comprises to receive audio input from the audiosensor.

Example 35 includes the subject matter of any of Examples 28-34, andwherein: to display the avatar in the ready state comprises to displayan anthropomorphic representation of eye contact of the avatar with theuser; and to display the avatar in the engaged state comprises todisplay an anthropomorphic representation of eye contact of the avatarwith the user.

Example 36 includes the subject matter of any of Examples 28-35, andfurther comprising an eye tracking sensor, wherein to display theanthropomorphic representation of eye contact comprises to: receive atleast one of eye tracking data or head position data from the eyetracking sensor; and display an anthropomorphic representation of eyesof the avatar following the user.

Example 37 includes the subject matter of any of Examples 28-36, andwherein: to display the avatar of the virtual personal assistant in thedisengaged state comprises to display the avatar as semitransparent, toallow a user interface element of the computing device to shine throughthe avatar; to display the avatar in the ready state comprises todecrease a transparency of the avatar; and to display the avatar in theengaged state comprises one of to: decrease the transparency of theavatar or eliminate the transparency of the avatar.

Example 38 includes the subject matter of any of Examples 28-37, andwherein: to display the avatar of the virtual personal assistant in thedisengaged state comprises to display the avatar at a position on thedisplay away from an active application of the computing device; todisplay the avatar in the ready state comprises to move the avatar onthe display to a position closer to an active application of thecomputing device; and to display the avatar in the engaged statecomprises to move the avatar on the display to a position over an activeapplication of the computing device without preventing input from theuser to the active application.

Example 39 includes the subject matter of any of Examples 28-38, andfurther comprising an eye tracking sensor, wherein: to display theavatar in the ready state comprises to move the avatar on the display toa position closer to a position where the user is focused, based on eyetracking data received from the eye tracking sensor; and to display theavatar in the engaged state comprises to move the avatar on the displayto a position closer to a position where the user is focused, based oneye tracking data received from the eye tracking sensor.

Example 40 includes the subject matter of any of Examples 28-39, andwherein: to display the avatar of the virtual personal assistant in thedisengaged state comprises to display the avatar as invisible; and todisplay the avatar in the ready state comprises to display the avatar asvisible.

Example 41 includes the subject matter of any of Examples 28-40, andwherein the virtual personal assistant is further to: perform speechrecognition while the avatar is in the engaged state; determine whetherthe user has issued a command based on the speech recognition; anddisplay the avatar in a working state in response to a determinationthat the user has issued the command.

Example 42 includes the subject matter of any of Examples 28-41, andwherein to display the avatar in the working state comprises at leastone of to: (i) increase a transparency of the avatar or (ii) move theavatar on the display to a position away from an active application ofthe computing device.

Example 43 includes a method for speech recognition on a computingdevice, the method comprising: capturing audio input using an audiosensor of the computing device; distorting the audio input to produce aplurality of distorted audio variations; performing speech recognitionon the audio input and each of the distorted audio variations to producea plurality of speech recognition results; and selecting a result fromthe speech recognition results based on contextual information.

Example 44 includes the subject matter of Example 43, and whereindistorting the audio input comprises removing an internal segment of theaudio input.

Example 45 includes the subject matter of any of Examples 43 and 44, andwherein removing the internal segment of the audio input comprisesremoving a segment of the audio input having an amplitude with apredefined relationship to an amplitude threshold.

Example 46 includes the subject matter of any of Examples 43-45, andwherein removing the internal segment comprises removing the segmenthaving an amplitude below the amplitude threshold.

Example 47 includes the subject matter of any of Examples 43-46, andwherein distorting the audio input comprises expanding a length of asegment of the audio input having an amplitude with a predefinedrelationship to an amplitude threshold.

Example 48 includes the subject matter of any of Examples 43-47, andwherein expanding the length of the segment comprises expanding a lengthof the segment having amplitude below the amplitude threshold.

Example 49 includes the subject matter of any of Examples 43-48, andwherein distorting the audio input comprises inserting a pause at aphonetic split point of the audio input identified by performing speechrecognition on the audio input.

Example 50 includes the subject matter of any of Examples 43-49, andwherein distorting the audio input comprises performing at least one of:(i) speeding up the audio input, (ii) slowing down the audio input,(iii) adjusting a pitch of the audio input, or (iv) introducing noise tothe audio input.

Example 51 includes the subject matter of any of Examples 43-50, andwherein performing speech recognition on the audio input and thedistorted audio variations to produce the plurality of speechrecognition results comprises performing speech recognition on the audioinput and the distorted audio variations to produce at least one hundredspeech recognition results.

Example 52 includes the subject matter of any of Examples 43-51, andfurther comprising determining semantically relevant results of thespeech recognition results based on a speech recognition grammar of oneor more applications of the computing device; wherein selecting theresult from the speech recognition results comprises selecting a resultfrom the semantically relevant results.

Example 53 includes the subject matter of any of Examples 43-52, andwherein determining the semantically relevant results based on thespeech recognition grammar of the one or more applications comprisesdetermining the semantically relevant results based on a speechrecognition grammar of a virtual personal assistant of the computingdevice.

Example 54 includes a method for speech recognition on a computingdevice, the method comprising: capturing audio input using an audiosensor of the computing device; distorting, on the computing device, theaudio input to produce a plurality of distorted audio variations;transmitting the audio input and the distorted audio variations from thecomputing device to a speech recognition module; receiving a pluralityof speech recognition results from the speech recognition module basedon the audio input and the distorted audio variations; and selecting, onthe computing device, a result from the speech recognition results basedon contextual information.

Example 55 includes the subject matter of Example 54, and wherein:transmitting the audio input and the distorted audio variations to thespeech recognition module comprises transmitting the audio input and thedistorted audio variations to a speech recognition module located on aserver remote from the computing device; and receiving the plurality ofspeech recognition results from the speech recognition module comprisesreceiving the plurality of speech recognition results from the speechrecognition module located on the server.

Example 56 includes the subject matter of any of Examples 54 and 55, andwherein distorting the audio input comprises removing an internalsegment of the audio input.

Example 57 includes the subject matter of any of Examples 54-56, andwherein removing the internal segment of the audio input comprisesremoving a segment of the audio input having an amplitude with apredefined relationship to an amplitude threshold.

Example 58 includes the subject matter of any of Examples 54-57, andwherein removing the internal segment comprises removing the segmenthaving an amplitude below the amplitude threshold.

Example 59 includes the subject matter of any of Examples 54-58, andwherein distorting the audio input comprises expanding a length of asegment of the audio input having an amplitude with a predefinedrelationship to an amplitude threshold.

Example 60 includes the subject matter of any of Examples 54-59, andwherein expanding the length of the segment comprises expanding a lengthof the segment having amplitude below the amplitude threshold.

Example 61 includes the subject matter of any of Examples 54-60, andwherein distorting the audio input comprises inserting a pause at aphonetic split point of the audio input identified by performing speechrecognition on the audio input.

Example 62 includes the subject matter of any of Examples 54-61, andwherein distorting the audio input comprises performing at least one of:(i) speeding up the audio input, (ii) slowing down the audio input,(iii) adjusting a pitch of the audio input, or (iv) introducing noise tothe audio input.

Example 63 includes the subject matter of any of Examples 54-62, andwherein performing speech recognition on the audio input and thedistorted audio variations to produce the plurality of speechrecognition results comprises performing speech recognition on the audioinput and the distorted audio variations to produce at least one hundredspeech recognition results.

Example 64 includes a method for speech recognition on a speechrecognition server, the method comprising: receiving, on the speechrecognition server, audio input captured by a computing device;distorting, on the speech recognition server, the audio input to producea plurality of distorted audio variations; performing, on the speechrecognition server, speech recognition on the audio input and each ofthe distorted audio variations to produce a plurality of speechrecognition results; and transmitting the plurality of speechrecognition results to the computing device.

Example 65 includes the subject matter of Example 64, and whereindistorting the audio input comprises removing an internal segment of theaudio input.

Example 66 includes the subject matter of any of Examples 64 and 65, andwherein removing the internal segment of the audio input comprisesremoving a segment of the audio input having an amplitude with apredefined relationship to an amplitude threshold.

Example 67 includes the subject matter of any of Examples 64-66, andwherein removing the internal segment comprises removing the segmenthaving an amplitude below the amplitude threshold.

Example 68 includes the subject matter of any of Examples 64-67, andwherein distorting the audio input comprises expanding a length of asegment of the audio input having an amplitude with a predefinedrelationship to an amplitude threshold.

Example 69 includes the subject matter of any of Examples 64-68, andwherein expanding the length of the segment comprises expanding a lengthof the segment having amplitude below the amplitude threshold.

Example 70 includes the subject matter of any of Examples 64-69, andwherein distorting the audio input comprises inserting a pause at aphonetic split point of the audio input identified by performing speechrecognition on the audio input.

Example 71 includes the subject matter of any of Examples 64-70, andwherein distorting the audio input comprises performing at least one of:(i) speeding up the audio input, (ii) slowing down the audio input,(iii) adjusting a pitch of the audio input, or (iv) introducing noise tothe audio input.

Example 72 includes the subject matter of any of Examples 64-71, andwherein performing speech recognition on the audio input and thedistorted audio variations to produce the plurality of speechrecognition results comprises performing speech recognition on the audioinput and the distorted audio variations to produce at least one hundredspeech recognition results.

Example 73 includes a method for interaction with a virtual personalassistant on a computing device, the method comprising: displaying anavatar of the virtual personal assistant in a disengaged state on adisplay of the computing device; determining, on the computing device,whether a user of the computing device has requested activation of thevirtual personal assistant; displaying, on the computing device, theavatar in a ready state in response to determining that the user hasrequested activation of the virtual personal assistant; determining, onthe computing device, an engagement level of the user while the avataris in the ready state; displaying, on the computing device, the avatarin an engaged state in response to the user having an engagement levelgreater than a threshold level; and displaying, on the computing device,the avatar in the disengaged state in response to the user having anengagement level less than the threshold level.

Example 74 includes the subject matter of Example 73, and furthercomprising monitoring the engagement level of the user while the avataris in the engaged state.

Example 75 includes the subject matter of any of Examples 73 and 74, andwherein determining whether the user has requested activation of thevirtual personal assistant comprises: receiving eye tracking data froman eye tracking sensor of the computing device; and determining whetherthe user has focused on the avatar based on the eye tracking data.

Example 76 includes the subject matter of any of Examples 73-75, andwherein determining whether the user has requested activation of thevirtual personal assistant comprises: receiving audio input from anaudio sensor of the computing device; and detecting a code word utteredby the user based on the audio input.

Example 77 includes the subject matter of any of Examples 73-76, andwherein determining the engagement level of the user comprises:receiving eye tracking data from an eye tracking sensor of the computingdevice; determining, based on the eye tracking data, whether the userhas visually focused on the avatar for a period of time greater than athreshold time; determining that the user has an engagement levelgreater than the threshold level in response to determining that theuser has visually focused on the avatar for a period of time greaterthan the threshold time; and determining that the user has an engagementlevel less than the threshold level in response to determining that theuser has not visually focused on the avatar for a period of time greaterthan the threshold time.

Example 78 includes the subject matter of any of Examples 73-77, andwherein determining whether the user has visually focused on the avatarfor a period of time greater than the threshold time comprises ignoringglances away from the avatar for a second period of time less than asecond threshold time.

Example 79 includes the subject matter of any of Examples 73-78, andwherein determining the engagement level of the user comprises receivingaudio input from an audio sensor of the computing device.

Example 80 includes the subject matter of any of Examples 73-79, andwherein: displaying the avatar in the ready state comprises displayingan anthropomorphic representation of eye contact of the avatar with theuser; and displaying the avatar in the engaged state comprisesdisplaying an anthropomorphic representation of eye contact with theuser.

Example 81 includes the subject matter of any of Examples 73-80, andwherein displaying the anthropomorphic representation of eye contactcomprises: receiving at least one of eye tracking data or head positiondata from an eye tracking sensor of the computing device; and displayingan anthropomorphic representation of eyes of the avatar following theuser.

Example 82 includes the subject matter of any of Examples 73-81, andwherein displaying the avatar of the virtual personal assistant in thedisengaged state comprises: displaying the avatar as semitransparent,allowing a user interface element of the computing device to shinethrough the avatar; displaying the avatar in the ready state comprisesdecreasing a transparency of the avatar; and displaying the avatar inthe engaged state comprises one of: decreasing the transparency of theavatar or eliminating the transparency of the avatar.

Example 83 includes the subject matter of any of Examples 73-82, andwherein: displaying the avatar of the virtual personal assistant in thedisengaged state comprises displaying the avatar at a position on thedisplay away from an active application of the computing device;displaying the avatar in the ready state comprises moving the avatar onthe display to a position closer to an active application of thecomputing device; and displaying the avatar in the engaged statecomprises moving the avatar on the display to a position over an activeapplication of the computing device without preventing input from theuser to the active application.

Example 84 includes the subject matter of any of Examples 73-83, andwherein: displaying the avatar in the ready state comprises moving theavatar on the display to a position closer to a position where the useris focused, based on eye tracking data received from an eye trackingsensor of the computing device; and displaying the avatar in the engagedstate comprises moving the avatar on the display to a position closer toa position where the user is focused, based on eye tracking datareceived from an eye tracking sensor of the computing device.

Example 85 includes the subject matter of any of Examples 73-84, andwherein: displaying the avatar of the virtual personal assistant in thedisengaged state comprises displaying the avatar as invisible; anddisplaying the avatar in the ready state comprises displaying the avataras visible.

Example 86 includes the subject matter of any of Examples 73-85, andfurther comprising: performing speech recognition while the avatar is inthe engaged state; determining whether the user has issued a commandbased on the speech recognition; and displaying the avatar in a workingstate in response to determining the user has issued the command.

Example 87 includes the subject matter of any of Examples 73-86, andwherein displaying the avatar in the working state comprises at leastone of: (i) increasing a transparency of the avatar or (ii) moving theavatar on the display to a position away from an active application ofthe computing device.

Example 88 includes a computing device comprising: a processor; and amemory having stored therein a plurality of instructions that whenexecuted by the processor cause the computing device to perform themethod of any of Examples 43-87.

Example 89 includes one or more machine readable storage mediacomprising a plurality of instructions stored thereon that in responseto being executed result in a computing device performing the method ofany of Examples 43-87.

Example 90 includes a computing device comprising means for performingthe method of any of Examples 43-87.

1. A computing device comprising: a display; a virtual personalassistant to display an avatar of the virtual personal assistant on thedisplay in a disengaged state, a ready state, and an engaged state; andan engagement module to: determine whether a user of the computingdevice has requested activation of the virtual personal assistant whilethe avatar is displayed in the disengaged state; determine an engagementlevel of the user while the avatar is displayed in the ready state; andmonitor the engagement level of the user while the avatar is displayedin the engaged state; wherein the virtual personal assistant is to:display the avatar in the ready state in response to a determinationthat the user has requested activation of the virtual personalassistant; display the avatar in the engaged state in response to adetermination that the user has an engagement level greater than athreshold level; and display the avatar in the disengaged state inresponse to a determination that the user has an engagement level lessthan the threshold level.
 2. The computing device of claim 1, furthercomprising an eye tracking sensor, wherein to determine whether the userhas requested activation of the virtual personal assistant comprises to:receive eye tracking data from the eye tracking sensor; and determinewhether the user has focused on the avatar based on the eye trackingdata.
 3. The computing device of claim 1, further comprising an eyetracking sensor, wherein to determine the engagement level of the usercomprises to: receive eye tracking data from the eye tracking sensor;determine, based on the eye tracking data, whether the user has visuallyfocused on the avatar for a period of time greater than a thresholdtime; determine that the user has an engagement level greater than thethreshold level in response to a determination that the user hasvisually focused on the avatar for a period of time greater than thethreshold time; and determine that the user has an engagement level lessthan the threshold level in response to a determination that the userhas not visually focused on the avatar for a period of time greater thanthe threshold time.
 4. The computing device of claim 1, wherein todisplay the avatar in the ready state and to display the avatar in theengaged state comprise to display an anthropomorphic representation ofeye contact of the avatar with the user.
 5. The computing device ofclaim 4, further comprising an eye tracking sensor, wherein to displaythe anthropomorphic representation of eye contact comprises to: receiveat least one of eye tracking data or head position data from the eyetracking sensor; and display an anthropomorphic representation of eyesof the avatar following the user.
 6. The computing device of claim 1,wherein: to display the avatar of the virtual personal assistant in thedisengaged state comprises to display the avatar as semitransparent, toallow a user interface element of the computing device to shine throughthe avatar; to display the avatar in the ready state comprises todecrease a transparency of the avatar; and to display the avatar in theengaged state comprises one of to: decrease the transparency of theavatar or eliminate the transparency of the avatar.
 7. The computingdevice of claim 1, further comprising an eye tracking sensor, wherein:to display the avatar in the ready state and to display the avatar inthe engaged state comprise to move the avatar on the display to aposition closer to a position where the user is focused, based on eyetracking data received from the eye tracking sensor.
 8. A methodcomprising: displaying, by a computing device, an avatar of a virtualpersonal assistant on a display of the computing device in a disengagedstate, a ready state, and an engaged state; determining, by thecomputing device, whether a user of the computing device has requestedactivation of the virtual personal assistant while the avatar isdisplayed in the disengaged state; determining, by the computing device,an engagement level of the user while the avatar is displayed in theready state; monitoring, by the computing device, the engagement levelof the user while the avatar is displayed in the engaged state;displaying, by the computing device, the avatar in the ready state inresponse to a determination that the user has requested activation ofthe virtual personal assistant; displaying, by the computing device, theavatar in the engaged state in response to a determination that the userhas an engagement level greater than a threshold level; and displaying,by the computing device, the avatar in the disengaged state in responseto a determination that the user has an engagement level less than thethreshold level.
 9. The method of claim 8, wherein determining whetherthe user has requested activation of the virtual personal assistantcomprises: receiving eye tracking data from an eye tracking sensor ofthe computing device; and determining whether the user has focused onthe avatar based on the eye tracking data.
 10. The method of claim 8,wherein determining the engagement level of the user comprises:receiving eye tracking data from an eye tracking sensor of the computingdevice; determining, based on the eye tracking data, whether the userhas visually focused on the avatar for a period of time greater than athreshold time; determining that the user has an engagement levelgreater than the threshold level in response to a determination that theuser has visually focused on the avatar for a period of time greaterthan the threshold time; and determining that the user has an engagementlevel less than the threshold level in response to a determination thatthe user has not visually focused on the avatar for a period of timegreater than the threshold time.
 11. The method of claim 8, whereindisplaying the avatar in the ready state and displaying the avatar inthe engaged state comprise displaying an anthropomorphic representationof eye contact of the avatar with the user.
 12. The method of claim 11,wherein displaying the anthropomorphic representation of eye contactcomprises: receiving at least one of eye tracking data or head positiondata from an eye tracking sensor of the computing device; and displayingan anthropomorphic representation of eyes of the avatar following theuser.
 13. The method of claim 8, wherein: displaying the avatar of thevirtual personal assistant in the disengaged state comprises displayingthe avatar as semitransparent, to allow a user interface element of thecomputing device to shine through the avatar; displaying the avatar inthe ready state comprises decreasing a transparency of the avatar; anddisplaying the avatar in the engaged state comprises one of decreasingthe transparency of the avatar or eliminating the transparency of theavatar.
 14. The method of claim 8, wherein: displaying the avatar in theready state and displaying the avatar in the engaged state comprisemoving the avatar on the display to a position closer to a positionwhere the user is focused, based on eye tracking data received from aneye tracking sensor of the computing device.
 15. One or more computerreadable storage media comprising a plurality of instructions storedthereon that, when executed by a computing device, cause the computingdevice to: display an avatar of a virtual personal assistant on adisplay of the computing device in a disengaged state, a ready state,and an engaged state; determine whether a user of the computing devicehas requested activation of the virtual personal assistant while theavatar is displayed in the disengaged state; determine an engagementlevel of the user while the avatar is displayed in the ready state;monitor the engagement level of the user while the avatar is displayedin the engaged state; display the avatar in the ready state in responseto a determination that the user has requested activation of the virtualpersonal assistant; display the avatar in the engaged state in responseto a determination that the user has an engagement level greater than athreshold level; and display the avatar in the disengaged state inresponse to a determination that the user has an engagement level lessthan the threshold level.
 16. The one or more computer readable media ofclaim 15, wherein to determine whether the user has requested activationof the virtual personal assistant comprises to: receive eye trackingdata from an eye tracking sensor of the computing device; and determinewhether the user has focused on the avatar based on the eye trackingdata.
 17. The one or more computer readable media of claim 15, whereinto determine the engagement level of the user comprises to: receive eyetracking data from an eye tracking sensor of the computing device;determine, based on the eye tracking data, whether the user has visuallyfocused on the avatar for a period of time greater than a thresholdtime; determine that the user has an engagement level greater than thethreshold level in response to a determination that the user hasvisually focused on the avatar for a period of time greater than thethreshold time; and determine that the user has an engagement level lessthan the threshold level in response to a determination that the userhas not visually focused on the avatar for a period of time greater thanthe threshold time.
 18. The one or more computer readable media of claim15, wherein to display the avatar in the ready state and to display theavatar in the engaged state comprise to display an anthropomorphicrepresentation of eye contact of the avatar with the user.
 19. The oneor more computer readable media of claim 18, wherein to display theanthropomorphic representation of eye contact comprises to: receive atleast one of eye tracking data or head position data from an eyetracking sensor of the computing device; and display an anthropomorphicrepresentation of eyes of the avatar following the user.
 20. The one ormore computer readable media of claim 15, wherein: to display the avatarof the virtual personal assistant in the disengaged state comprises todisplay the avatar as semitransparent, to allow a user interface elementof the computing device to shine through the avatar; to display theavatar in the ready state comprises to decrease a transparency of theavatar; and to display the avatar in the engaged state comprises one ofto decrease the transparency of the avatar or to eliminate thetransparency of the avatar.
 21. The one or more computer readable mediaof claim 15, wherein: to display the avatar in the ready state and todisplay the avatar in the engaged state comprise to move the avatar onthe display to a position closer to a position where the user isfocused, based on eye tracking data received from an eye tracking sensorof the computing device.